Job Title: Senior Data Scientist – AI Agents & LLM Architectures(Onsite)
Location: Woodland, Hills
Pay range: $ -$/hr
Overview
We are seeking a Senior Data Scientist with deep expertise in AI agent architectures, LLMs, and NLP to drive innovation in intelligent, interoperable healthcare platforms.
This role focuses on developing Agent-to-Agent (AA) protocols and Model Context Protocols (MCP) to enable context-aware, memory-augmented, and self-improving multi-agent systems across clinical, administrative, and benefits workflows.
Key Responsibilities
Design and implement Agent-to-Agent (AA) protocols for autonomous collaboration, negotiation, and task delegation between healthcare agents (, ClaimsAgent, EligibilityAgent, ProviderMatchAgent ).
Architect and operationalize Model Context Protocol (MCP) pipelines to support persistent, contextually grounded, multi-turn LLM interactions.
Build multi-agent orchestration systems with LLM-driven planning modules to optimize benefit processing, prior authorization, clinical summarization, and member engagement.
Fine-tune and integrate domain-specific LLMs and NLP models (, BioGPT, medical BERT) for document understanding, intent classification, and personalized plan recommendations.
Develop retrieval-augmented generation (RAG) systems and structured context libraries for grounding across structured standards (FHIR, ICD-) and unstructured data (EHR notes, chat logs).
Collaborate with engineering and data teams to deliver scalable, secure, and compliant agentic pipelines aligned with healthcare regulations (HIPAA, CMS, NCQA).
Lead research in memory-based agent systems, RLHF, and context-aware task planning.
Contribute to production deployment via robust MLOps pipelines (versioning, monitoring, continuous improvement).
Required Qualifications
Master’s or in Computer Science, Machine Learning, Computational Linguistics, or related field.
+ years in applied AI, specializing in LLMs, transformers, NLP, or agent frameworks in healthcare.
Hands-on experience with Agent-to-Agent protocols and multi-agent frameworks (, LangGraph, AutoGen, CrewAI).
Practical expertise in Model Context Protocols (MCP) for modular, long-lived agent interactions.
Strong Python skills with ML/NLP libraries (Hugging Face Transformers, PyTorch, LangChain, spaCy).
Familiarity with healthcare benefits systems (plans, claims, eligibility rules).
Knowledge of healthcare data standards (FHIR, HL, ICD/CPT, X EDI).
Cloud-native development (AWS, Azure, or GCP) with Kubernetes, Docker, CI/CD.
Preferred Qualifications
Deep knowledge of MCP + VectorDB integration for dynamic agent memory and retrieval.
Prior experience deploying LLM-based agents at scale in healthcare.
Exposure to voice AI, automated care navigation, or AI triage systems.
Published research or patents in LLM architectures, contextual AI, or agent systems.